NDLI: A discriminative approach for appearance based loop closing

Content Provider	IEEE Xplore Digital Library
Author	Ciarfuglia, T.A. Costante, G. Valigi, P. Ricci, E.
Copyright Year	2012
Description	Author affiliation: Department of Electrical and Information Engineering, University of Perugia, Via G. Duranti, 93, 06125, Italy (Ciarfuglia, T.A.; Costante, G.; Valigi, P.; Ricci, E.)
Abstract	The place recognition module is a fundamental component in SLAM systems, as incorrect loop closures may result in severe errors in trajectory estimation. In the case of appearance-based methods the bag-of-words approach is typically employed for recognizing locations. This paper introduces a novel algorithm for improving loop closures detection performance by adopting a set of visual words weights, learned offline accordingly to a discriminative criterion. The proposed weights learning approach, based on the large margin paradigm, can be used for generic similarity functions and relies on an efficient online leaning algorithm in the training phase. As the computed weights are usually very sparse, a gain in terms of computational cost at recognition time is also obtained. Our experiments, conducted on publicly available datasets, demonstrate that the discriminative weights lead to loop closures detection results that are more accurate than the traditional bag-of-words method and that our place recognition approach is competitive with state-of-the-art methods.
Starting Page	3837
Ending Page	3843
File Size	1323008
Page Count	7
File Format	PDF
ISBN	9781467317375
ISSN	21530858
e-ISBN	9781467317368
DOI	10.1109/IROS.2012.6385654
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-07
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Visualization Vocabulary Histograms Simultaneous localization and mapping Vectors
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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